Trends in User Identity and Continuous Authentication
Authors: 
Uélison Jean Lopes dos Santos, Cristiano André da Costa, André Henrique Mayer, Eduardo Souza dos Reis, Juan Eduardo Cruz Maldonado, Jorge Luis Victória Barbosa, Rodolfo Stoffel Antunes, Rodrigo da Rosa Righi, Nelson Eduardo FloresAuthors Info & Claims
Uélison Jean Lopes dos Santos, Cristiano André da Costa, André Henrique Mayer, Eduardo Souza dos Reis, Juan Eduardo Cruz Maldonado, Jorge Luis Victória Barbosa, Rodolfo Stoffel Antunes, Rodrigo da Rosa Righi, Nelson Eduardo FloresAuthors Info & ClaimsAbstract
The challenges of continuous authentication have resulted in a surge of recent studies. This article surveys the state of the art and rising trends in continuous and adaptive context-aware authentication. It employs a statistical approach to collect metrics on recent works exploring machine learning techniques for continuous learning.
References
[1]
H. Cao and K. C. C. Chang, “Nonintrusive smartphone user verification using anonymized multimodal data,”IEEE Trans. Knowl. Data Eng., vol. 31, no. 3, pp. 479–492, 2019.
[2]
F. Alt and S. Schneegass, “Beyond passwords—Challenges and opportunities of future authentication,”IEEE Security Privacy, vol. 20, no. 1, pp. 82–86, 2022.
[3]
X. Liang, F. Zou, L. Li, and P. Yi, “Mobile terminal identity authentication system based on behavioral characteristics,”Int. J. Distrib. Sensor Netw., vol. 16, no. 1, p. 1,550,147,719,899,371, 2020.
[4]
L. Gonzalez-Manzano, J. M. D. Fuentes, and A. Ribagorda, “Leveraging user-related internet of things for continuous authentication: A survey,”ACM Comput. Surv., vol. 52, no. 3, pp. 1–38, 2019.
[5]
W. Li, J. Cao, K. Hu, J. Xu, and R. Buyya, “A trust-based agent learning model for service composition in mobile cloud computing environments,”IEEE Access, vol. 7, pp. 34,207–34,226, Mar.2019.
[6]
H. Zhang, D. Singh, and X. Li, “Augmenting authentication with context-specific behavioral biometrics,” in Proc. 52nd Hawaii Int. Conf. Syst. Sci., 2019, p. 1.
[7]
Q. Zou, Y. Wang, Q. Wang, Y. Zhao, and Q. Li, “Deep learning-based gait recognition using smartphones in the wild,”IEEE Trans. Inf. Forensics Security, vol. 15, pp. 3197–3212, Apr.2020.
[8]
Y. Liang, S. Samtani, B. Guo, and Z. Yu, “Behavioral biometrics for continuous authentication in the internet-of-things era: An artificial intelligence perspective,”IEEE Internet Things J., vol. 7, no. 9, pp. 9128–9143, 2020.
[9]
Y. Lee and S. Cho, “Abnormal usage sequence detection for identification of user needs via recurrent neural network semantic variational autoencoder,”Int. J. Human-Comput. Interact., vol. 36, no. 7, pp. 631–640, 2020.
[10]
V. Shankar and K. Singh, “An intelligent scheme for continuous authentication of smartphone using deep auto encoder and softmax regression model easy for user brain,”IEEE Access, vol. 7, pp. 48,645–48,654, Apr.2019.
[11]
C. Wu, K. He, J. Chen, and R. Du, “ICAuth: Implicit and continuous authentication when the screen is awake,” in Proc. IEEE Int. Conf. Commun., May 2019, pp. 1–6.
[12]
D. Chen, Z. Ding, C. Yan, and M. Wang, “A behavioral authentication method for mobile based on browsing behaviors,”IEEE/CAA J. Automat. Sinica, vol. 7, no. 6, pp. 1528–1541, 2019.
[13]
J. Fenget al., “DPlink: User identity linkage via deep neural network from heterogeneous mobility data,” in Proc. World Wide Web Conf., Association for Computing Machinery, May 2019, pp. 459–469.
[14]
A. Mahfouz, T. M. Mahmoud, and A. S. Eldin, “A survey on behavioral biometric authentication on smartphones,”J. Inf. Security Appl., vol. 37, pp. 28–37, Dec.2017.
[15]
S. Alotaibi, A. Alruban, S. Furnell, and N. L. Clarke, “A novel behaviour profiling approach to continuous authentication for mobile applications,” in Proc. 5th Int. Conf. Inf. Syst. Security Privacy, 2019, pp. 246–251.
[16]
T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,”IEEE Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018.
[17]
O. Alpar, “TAPSTROKE: A novel intelligent authentication system using tap frequencies,”Expert Syst. Appl., vol. 136, no. C, pp. 426–438, 2019.
[18]
R. Rocha, D. Carneiro, and P. Novais, “Continuous authentication with a focus on explainability,”Neurocomputing, vol. 423, pp. 697–702, Jan.2020.
[19]
L. Xiaofeng, Z. Shengfei, and Y. Shengwei, “Continuous authentication by free-text keystroke based on CNN plus RNN,”Procedia Comput. Sci., vol. 147, pp. 314–318, Feb.2019.
[20]
M. Abuhamad, T. Abuhmed, D. Mohaisen, and D. H. Nyang, “AUTOSen: Deep-learning-based implicit continuous authentication using smartphone sensors,”IEEE Internet Things J., vol. 7, no. 6, pp. 5008–5020, 2020.
[21]
R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, and F. Scotti, “Deep-ECG: Convolutional neural networks for ECG biometric recognition,”Pattern Recognit. Lett., vol. 126, pp. 78–85, Sep.2019.
[22]
Y. Li, H. Hu, Z. Zhu, and G. Zhou, “SCANet: Sensor-based continuous authentication with two-stream convolutional neural networks,”ACM Trans. Sensor Netw., vol. 16, no. 3, pp. 1–27, 2020.
[23]
L. Chen, Y. Zhong, and D. Zhang, “Continuous authentication based on user interaction behavior,” in Proc. 2019 7th Int. Symp. Digit. Forensics Security (ISDFS), pp. 1–6.
[24]
J. H. Addae, X. Sun, D. Towey, and M. Radenkovic, “Exploring user behavioral data for adaptive cybersecurity,”User Model. User-Adapted Interact., vol. 29, no. 3, pp. 701–750, Jul.2019.
[25]
J. Solano, L. Camacho, A. Correa, C. Deiro, J. Vargas, and M. Ochoa, “Risk-based static authentication in web applications with behavioral biometrics and session context analytics,” in Applied Cryptography and Network Security Workshops, vol. 11605. Cham: Springer Nature Switzerland AG, 2019, pp. 3–23.
[26]
H. C. Volaka, G. Alptekin, O. E. Basar, M. Isbilen, and O. D. Incel, “Towards continuous authentication on mobile phones using deep learning models,”Procedia Comput. Sci., vol. 155, pp. 177–184, Jan.2019.
[27]
R. G. Pensa, G. D. Blasi, and L. Bioglio, “Network-aware privacy risk estimation in online social networks,”Social Netw. Anal. Mining, vol. 9, no. 1, Dec.2019, Art. no. 15.
[28]
J. Tan, M. Sharif, S. Bhagavatula, M. Beckerle, M. L. Mazurek, and L. Bauer, “Comparing hypothetical and realistic privacy valuations,” in Proc. ACM Conf. Comput. Commun. Security, Association for Computing Machinery, Oct. 2018, pp. 168–182.
[29]
X. Chen, Y. Wang, J. He, S. Pan, Y. Li, and P. Zhang, “CAP: Context-aware app usage prediction with heterogeneous graph embedding,”Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 3, no. 1, pp. 1–25, 2019.
[30]
H. Fang, X. Wang, and S. Tomasin, “Machine learning for intelligent authentication in 5G and beyond wireless networks,”IEEE Wireless Commun., vol. 26, no. 5, pp. 55–61, 2019.
Index Terms
- Trends in User Identity and Continuous Authentication
Index terms have been assigned to the content through auto-classification.
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Published: 01 November 2022
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